A team of chemical and biological engineers at Seoul National University in the Republic of Korea has developed a proof-of-concept device that could one day lead to the creation of an artificial nose.
Category: robotics/AI – Page 445
Researchers have developed a machine learning model that generates quantum circuits from text descriptions, similar to how models like Stable Diffusion create images. This method, improves the efficiency and adaptability of quantum computing.
One of the most important recent developments in Machine Learning (ML) is generative models such as diffusion models. These include Stable Diffusion and Dall.e, which are revolutionizing the field of image generation. These models are able to produce high-quality images based on text descriptions.
“Our new model for programming quantum computers does the same but, instead of generating images, it generates quantum circuits based on the text description of the quantum operation to be performed,” explains Gorka Muñoz-Gil from the Department of Theoretical Physics of the University of Innsbruck, Austria.
Explore 10 unique applications of the convolutional neural network that highlights its revolutionary impact on technology.
What is your data worth to GPT?
AI Logging for Interpretability and Explainability🔬 — logix-project/logix.
🚨 TECH NEWS: The automaker will deploy Apptronik’s “Apollo” robot at a Mercedes factory in Hungary. Details:
German automaker Mercedes-Benz is deploying Apptronik’s Apollo robots at a manufacturing plant in Hungary.
Tesla has finally decided to release its Autopilot safety data report after taking a break of more than a year.
For years, Tesla used to release a “Vehicle safety report” that tracked miles between accidents in its vehicles based on the level of Autopilot used or not used and compared it to the industry average.
The automaker used the report to claim that its Autopilot technology resulted in a much safer driving experience and that its vehicles would crash much less often than the average car in the US even without Autopilot.
The rhetoric over “superhuman” AI implicitly erases what’s most important about being human.
Still, ChatGPT operates in a mostly siloed fashion. It can’t yet venture out “into the wild” to execute online tasks. For example, if you wanted to buy a milk frother on Amazon for under $100, ChatGPT might be able to recommend a product or two, and even provide links, but it can’t actually navigate Amazon and make the purchase.
Why? Besides obvious concerns, like letting a flawed AI model go on a shopping spree with your credit card, one challenge lies in training AI to successfully navigate graphical user interfaces (GUIs), like your laptop or smartphone screen.
But even the current version of GPT-4 seems to grasp the basic steps of online shopping. That’s the takeaway of a recent preprint paper in which AI researchers described how they successfully trained a GPT-4-based agent to “buy” products on Amazon. The agent, dubbed the MM-Navigator, did not actually purchase products, but it was able to analyze screenshots of an iOS smartphone screen and specify the appropriate action and where it should click, with impressive accuracy.
“The fear is that they can’t afford to let someone else get there first,” said Scott Jenson, a UX designer who left Google last month.
Apptronik, a NASA-backed robotics company, has unveiled Apollo, a humanoid robot that could revolutionize the workforce — because there’s virtually no limit to the number of jobs it can do.
“The focus for Apptronik is to build one robot that can do thousands of different things,” Jeff Cardenas, the company’s co-founder and CEO, told Freethink. “The best way to think of it is kind of like the iPhone of robots.”
The challenge: Robots have been automating repetitive tasks for decades — instead of having a person weld the same two car parts together 100 times a day, for example, an automaker might just add a welding robot to that segment of the assembly line.